Comparative assessment of the cost-effectiveness of Tuberculosis (TB) active case-finding interventions: A systematic analysis of TB REACH wave 5 projects

Purpose Interventions that can help streamline and reduce gaps in the tuberculosis (TB) care cascade can play crucial roles in TB prevention and care, but are often operationally complex and resource intensive, given the heterogenous settings in which they are implemented. In this study, we present a comparative analysis on cost-effectiveness of TB REACH Wave 5 projects with diverse programmatic objectives to inform future decisions regarding funding, strategic adoption, and scale-up. Methods We comprehensively reviewed project reports and financial statements from TB REACH Wave 5, a funding mechanism for interventions that aimed to strengthen the TB care cascade in diverse settings. Two independent reviewers abstracted cost (in 2017 US dollars) and key programmatic data, including project type (case-finding only; case-finding and linkage-to-care; or case-finding, linkage-to-care and patient support), operational setting (urban or rural), and project outputs (numbers of people with TB diagnosed, started on treatment, and successfully completing treatment). Cost-effectiveness ratios for each project were calculated as ratios of apportioned programmatic expenditures to corresponding project outputs. Results Of 32 case finding and patient support projects funded through TB REACH Wave 5, 29 were included for analysis (11 case-finding only; 9 case-finding and linkage-to-care; and 9 case-finding, linkage-to-care and patient support). 21 projects (72%) were implemented in either Africa or Southeast Asia, and 19 (66%) focused on serving urban areas. Average cost-effectiveness was $184 per case diagnosed (range: $30-$10,497), $332 per diagnosis and treatment initiation ($123-$10,608), and $40 per patient treatment supported ($8-$160). Cost per case diagnosed was lower for case-finding-only projects ($132) than projects including linkage-to-care ($342) or linkage-to-care and patient support ($254), and generally increased with the corresponding country’s per-capita GDP ($543 per $1000 increase, 95% confidence interval: -$53, $1138). Conclusion The costs and cost-effectiveness of interventions to strengthen the TB care cascade were heterogenous, reflecting differences in context and programmatic objective. Nevertheless, many such interventions are likely to offer good value for money. Systematic collection and analysis of cost-effectiveness data can help improve comparability, monitoring, and evaluation.

Introduction Tuberculosis (TB) is the second leading infectious cause of morbidity and mortality worldwide, trailing after SARS-CoV-2, with an estimated 9.9 million new TB cases and 1.5 million deaths in 2020 [1]. In 2014, the World Health Organization's End TB Strategy called for a 90% reduction in TB incidence and 95% reduction in TB mortality rates by 2035 [2]. Similarly, the Stop TB Partnership's Global Plan to End TB, launched in 2019, calls for UN member states to successfully treat 40 million people with TB and provide TB preventive therapy to at least 30 million people by 2022 [3]. But despite these ambitious goals, TB incidence and mortality are falling at no more than 2-4% per year-far below the reduction needed (>10%) to achieve global targets [1].
Currently, it is estimated that about 30% of people who develop active TB every year will not be notified to public health authorities-largely reflecting underdiagnosis and undertreatment [4]. As people with TB who are missed can perpetuate transmission and suffer the adverse consequences of untreated disease (including death), it is imperative to identify these individuals early and ensure the rapid uptake of TB treatment, particularly among at-risk populations. Public health interventions such as intensified case finding (ICF), active case finding (ACF) and other approaches to improve gaps in the TB care cascade are therefore critical components of a comprehensive strategy to reduce the burden of TB worldwide [5,6].
Since 2010, the TB REACH initiative of the Stop TB partnership (UNOPS), supported by Global Affairs Canada, USAID, and the Bill and Melinda Gates Foundation, has funded 8 waves consisting of 313 projects in 54 countries that focus on adopting innovative (both technological and process) approaches to improve TB case detection and treatment. In particular, Wave 5 -the focus of this analysis-focused on innovative approaches to case finding. These projects have made important contributions in innovating and promoting TB case finding activities in many high-burden TB countries [7,8]. However, TB case finding and treatment projects are resource intensive, and their cost-effectiveness-both in absolute and comparative terms-remains uncertain [9,10].
Given the substantial investment made in these projects (over 155 million USD since the inception of the initiative), it is critical to understand the relative value generated by different types of case finding interventions. In this study, we used the project database of the TB REACH Wave 5 funded projects to comprehensively and systematically evaluate costs and cost-effectiveness across the wide range of case finding and treatment support projects supported by this initiative. In doing so, we compared cost-effectiveness ratios (CERs) [6] assessed for common outcome units that are most direct and readily calculated to measure ACF performance based on each project's scope of operations (1. TB case finding; 2. Linkage to care; and 3. TB treatment support) to rank and compare various factors influencing CERs.

Methods
During the wave 5 funding cycle, TB REACH funded 32 case finding projects across 20 different countries with total support of 16 million USD. An additional six small grants were provided to develop tools or products. While the main focus of the funding cycle was TB case detection, the overall scope of projects funded was broad; some examples of these innovations included novel approaches to case finding (e.g. recruiting civilians as TB finders in the community and strengthening public-private mix (PPM) partnerships), scaling-up previously proven concepts (e.g., engaging community health workers in rural areas in active case finding), improving treatment referral and adherence among individuals diagnosed with TB through active case finding, and increasing awareness regarding TB infection in the community (e.g. involvement of mass media, implementation of educational programs, and community engagement).

Screening and data extraction
We first obtained a complete set of administrative documentation-including project applications, reviews of project activity and financial reports-for each project funded during TB REACH wave 5. We then created a standardized data extraction spreadsheet, the components of which were based on a complete review of 32 selected case finding projects prior to data extraction.
Wave 5 projects that did not report relevant cost or program yield/performance data to TB REACH were excluded from the study. After consultation with TB REACH technical officers, three additional projects were excluded from the analysis. The first project (NTRL, EPHI) was a lab-based assessment of a novel transport and decontaminating reagent for TB testing, called OMNIgene1 SPUTUM. The second project (Ifakara Health Institute) was intended to assess the use of Xpert Omni/Xpert MTB Ultra cartridges. However, these cartridges were unavailable during the wave 5 funding cycle; therefore, the project was not able to begin activities. The third project (AIGHD) aimed to establish TB screening in an HIV community testing project that was similarly postponed.
Two authors (IG and CD) independently performed the data extraction by reviewing all relevant documentation and data for each project. All disagreements between the two authors were resolved by discussion. If a consensus could not be reached, the two senior investigators (DD and HS) were consulted. During these meetings, the four authors re-evaluated the financial report in question and/or sought additional information by the TB REACH technical team (PV, AK, and JC), who provided further detail and clarification on data discrepancies and any project-specific interpretations (e.g. successes and/or challenges reported by each project impacting interpretation of data parameters).
For each program, we assigned a unique code (Table 1) and extracted data from the finalized financial statement, determined based on the last update date for each project's financial statement (Dec 31 st , 2018).
Variables directly collected from financial statements included the characteristics of each program, the country in which the project was executed, a brief description of the program's  c. Projects are categorized into urban or rural settings based on the primary implementation environment. Targeted population is specified according to TB REACH narrative reports. d. Projects are considered as treatment related when they include treatment initiation or/and adherence activities. https://doi.org/10.1371/journal.pone.0270816.t001

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Cost-effectiveness of tuberculosis case-finding and treatment initiation interventions Pediatric Cases, and 5. Door-to-Door Screening. We also noted the projects that supplied or linked patients to preventive therapy and projects implementing ICF activities (Table 2); however, specific data or information to apportion costs or assess programmatic yields for preventive therapy were not explicitly reported. Therefore, cost-effectiveness ratios could not be estimated for provision of preventive therapy. Additionally, we extracted data on project service outputs, including number of people diagnosed with any type of TB, number of people started on TB treatment (notifications), and number of patients successfully treated (Table 3, S1 and S3 Tables in S1 File).

Data analysis
We categorized projects based on their respective WHO regions, national gross domestic product (GDP) per capita (reported in 2017 US dollars [11]), and project setting (urban versus rural/remote). Based on a review of all TB REACH wave 5 projects, we defined three types of programmatic activities-1. Case Finding Only, 2. Case Finding and Linkage-to-Care and 3. Case Finding, Linkage-to-Care and Patient Support (Box 1)-and assigned each project to one of these categories [2]. The main outcome of our analysis was the cost-effectiveness ratio (CER), calculated as the total estimated cost, assessed based on the cumulative expenditure, as reported by each project's financial statement, divided by the number of relevant service outputs (beneficiaries served). In summarizing CERs across multiple projects, we calculated an average CER across all contributing projects (i.e., total cost of all projects divided by total beneficiaries served). This is equivalent to a weighted average of each program's cost-effectiveness ratio, weighted by the number of beneficiaries.

Box 1. Categories of program activities in TB REACH wave 5
Case Finding: Program activities aim to register the target population and screen people with symptoms of TB. Activities include population enrollment and systematic symptom screening. Screening may be conducted in community settings through door-to-door visits of households or risk groups (active case finding) or in facility settings through passive surveillance. Screening tools may use a mobile phone or tablet-based platform. Some projects also used mobile diagnostic technologies such as mobile X-ray with computer aided diagnosis (CXR-CAD) and GeneXpert machines installed in mobile vans/ trucks. A select few projects also explored use of novel sample transport technologies such as drones to improve case finding. "Case finding only" projects may also provide at-risk patients with preventive therapy; however, they are not directly involved in treatment support or adherence (i.e. intensive patient follow-up).
Linkage-to-Care: Program activities aim to improve patients' linkage to care post diagnosis (refer patients for treatment initiation). Activities include open access tents which serve as the first stop point in the clinics for patients referred from different part of the clinic or community screening, outpatient care or hospitalization based on the severity of TB conditions. Patient Support: Program activities aim to improve management of patients' TB treatment and drug adherence. For example, to minimize loss to follow-up in treatment initiation, some programs further engaged newly diagnosed TB patients via follow-up phone calls, home visits, or peer support. For our primary analysis, we defined cumulative expenditure as the total cost of human resources, program activities, procurement of medical items, procurement of non-medical items, and direct program support, minus the cost of operational research (categories specified in each project's financial statement). If a project's data on cumulative expenditure was limited, we used income reports instead. Depending on each project's programmatic scope and components, we calculated CERs for each category of programmatic activities/outputs (Table 3 and S1-S4 Tables in S1 File): 1) case finding (cost per patient diagnosed), 2) linkageto-care (cost per patient referred for treatment), and 3) patient support (cost per patient completing treatment). For projects reporting programmatic activities beyond case finding (i.e., activities for linkage to care and/or treatment adherence), we assessed cost estimates for each programmatic activity by first assessing expense records specific to each activity (e.g. Xpert cartridge costs were considered specific to case finding costs only) and then adding apportioned shared costs based on the ratios of programmatic outputs for each major activity (i.e. ratios of number of people diagnosed with TB, number initiated on TB treatment and/or number of patients successfully completing TB treatment). CERs were calculated for projects individually, and (as described above) as weighted averages across projects conducting similar activities (e.g. projects involving case finding only). A simple linear regression was conducted to assess the association between CERs and country's per-capita GDP. Sensitivity analyses were performed by varying the total costs, as well as each of the different service outputs, by +/-25% independently for each project to evaluate the potential sensitivity of the results to those outcomes. This was done for projects conducting case finding only activities, as well as projects conducting case finding and treatment-related activities.

Results
Of the 29 projects included in our analysis, 11 solely focused on case finding while 18 had additional programmatic aims (nine included linkage-to-care, and nine also included patient treatment support). Most projects were implemented in the African region (n = 12, 41%) or South-East Asia region (n = 9, 31%). Ten projects (34%) were implemented in rural areas, and the other 19 focused on urban settings. In addition, 11 projects (38%) specifically targeted vulnerable populations (e.g. internally displaced persons, children, miners, female sex workers, people living with HIV, pregnant women, etc.) 8 projects (28%) focused on scalability ( Table 1).
The weighted average CER was $184 (Range: $30 -$10,497; n = 29) per TB case detected across all projects (Table 3). For case-finding only projects, the average cost per case detected was $132 (Range: $30 -$10,497). For projects that included both case finding and treatment initiation, the weighted CER was $342 (Range: $103 -$9,913). Projects with additional programmatic efforts toward treatment adherence and patient support had a weighted mean cost per case detected of $254 (Range: $141 -$3,233).
For those projects that included linkage to care efforts after TB diagnosis, the average cost per patient referred for treatment was $30 (Range: $8 -$695) (S2 Table in S1 File). For projects that included treatment adherence and patient support programs, the average cost per TB patient completing the treatment was $40 (Range: $8 -$160) (S3 Table in S1 File).
Six projects were identified as projects with CERs above a $1,000-per-case-detected threshold. Two of these projects-NAANK and ASOCI-included treatment support efforts and were implemented in upper-middle income countries. Thus, only three case-finding only projects (CHEAS, IRDSA, and FUNDA) and one project with linkage-to-care (GLOHI) had an estimated cost per case diagnosed higher than the corresponding country's per-capita GDP (Tables 1 and 3). Programmatic setbacks were the potential reasons for these inflated costs and will be discussed further in the Discussion section.
CERs also varied with characteristics of the underlying setting. Projects in urban settings had lower CERs than those in rural contexts (e.g., $169 vs. $242 per case detected). Moreover, the CERs of projects in the African region were generally higher than of projects performed in Southeast Asia ($554 vs. $95 per case detected) (Fig 1). This finding reflected two data trends. First, a small number of African projects had very high CERs, and these projects had greater influence on average CER values. Second, on average, projects from Southeast Asian countries diagnosed more people with TB, thereby lowering the estimated cost per TB case diagnosed compared to projects from the African region (Table 3). Cost per case diagnosed increased with the corresponding country's per-capita GDP ($543 per $1000 increase, 95% confidence interval: -$53, $1138).
This plot illustrates the cost-effectiveness ratio (2017 US dollars per case of tuberculosis diagnosed) associated with each project, according to the gross domestic product (GDP) per capita in each corresponding country. Letters represent the geographic region in which the projects were performed, and numbers order projects from largest (1) to smallest within each region. In each panel, there was one project that was not shown because its associated costeffectiveness ratio was exceptionally high (Panel A, project A10/IRDSA, cost per case diagnosed $10,497, GDP per capita: $6,133; Panel B, project A12/GLOHI, cost per case diagnosed $9,913, GDP per capita: $515).
In sensitivity analysis, variation in effectiveness estimates tended to have greater influence on estimated CERs than variation in costs. Varying both costs and outcomes by +/-25% did

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not affect on the characterization of projects as cost-effective (based on a cost per case diagnosed below a threshold of GDP per capita), with the sole exception of the ACREO project, which fell above this threshold when total costs increased, or case detection decreased by 25% (S1 and S3 Tables in S1 File).

Discussion
This comparative assessment of 29 projects designed to strengthen the TB care cascade highlights the heterogeneity in cost and cost-effectiveness observed when implementing interventions with similar aims in diverse contexts. Specifically, while the majority of projects diagnosed people with TB at a cost of less than $1000 per case detected, quantitative estimates varied over 100-fold depending on the local setting, target population, specific technology or other intervention employed, methodology of implementation and assessment, and objective. These findings demonstrate the importance of considering local context and realities of implementation when evaluating cost-effectiveness and argue against making blanket statements about the costeffectiveness of certain interventions (e.g., TB case detection). Furthermore, our specific results can be helpful to implementers and funders seeking to introduce interventions to strengthen the TB cascade of care in cost-effective fashion across a variety of diverse settings.
Despite this heterogeneity, important generalizable insights were discernible in these data. ACF projects included in our study had an overall weighted average cost per case detected under $300, below the midpoint of the corresponding opportunity-cost-based cost-effectiveness thresholds (CETs) for low-and-middle-income countries as estimated by Woods et al [12]. In most high-burden contexts, the long-term cost per disability-adjusted life year (DALY) averted has been estimated to be only modestly higher than the cost per case detected through active case-finding [6]. Thus, it is likely that these interventions would fall below country-specific CETs assessed in terms of GDP per capita in most settings with high TB burden. CETs have known limitations, and these data should not be used on their own to suggest that any specific TB case-finding intervention is cost-effective [13]. Nevertheless, these data can provide some guidance regarding the value for money of interventions to strengthen the cascade of TB diagnosis and care. Cost-effectiveness ratios were lower for projects implemented in low-income settings (where TB incidence is higher) and rural areas. Targeting hard-to-reach populations was generally not associated with an increase in cost per case detected. The cost of treatment support was generally lower than the cost to diagnose a TB case, suggesting that closing case-finding gaps requires considerably larger resource dedication than support of patients who have already been diagnosed and started on treatment. Costeffectiveness is also more favorable in settings with higher TB incidence, as the costs of screening result in more people with TB detected and treated. These findings can help funders and policymakers prioritize project implementation and evaluation in the future. In particular, for global funding mechanisms such as TB REACH and Global Fund, interventions focused on the most disadvantaged populations (e.g., the rural poor in low-income settings) may offer optimal value for money.
Our methodology-including systematic extraction of data from standardized financial and annual reports-facilitates comparison across projects and may be useful to funding agencies when seeking to draw comparative insight on cost-effectiveness across a pre-defined set of projects. As an external benchmark of the validity of this approach, we compared our estimates with the results from a recent study by Jo et al which comprehensively evaluated the costs and cost-effectiveness of one of the projects included in this analysis (CIDRZ Zambia) [14]. Jo and colleagues used a much more detailed approach to estimate the costs of each programmatic component of this intervention and reported a cost of $435 per patient initiating treatment [14]. Our simplified budget/finance statement-based calculation of the CER for this project was $420 per TB case diagnosed and $486 per patient initiating treatment, thus showing good agreement. The similarity of these findings may add a degree of external validation to the simple finance-report-based calculations performed here.
Four projects were identified as having higher cost-effectiveness ratios. On deeper review, these projects each confronted major operational difficulties during implementation. CHEAS experienced a major delay in implementation owing to challenges in hiring and retaining staff and underlying political unrest. IRDSA reported challenges in making household visits due to security concerns and a lower-than-expected number of patients with undiagnosed TB seeking services in the clinics. GLOHI reported that TB incidence in their region may have been overestimated (thereby resulting in fewer patients identified) and experienced multiple technical failures in drone usage, which hindered efficiency and implementation. The FUNDA project was substantially delayed in receipt of ethical approval, leading to lower-than-expected rates of TB diagnosis. These logistical challenges speak to the unpredictable nature of scaling up health interventions in real-world settings and the resulting variation in cost-effectiveness that will likely be observed in actual implementation. These project-specific findings should not be interpreted as favoring one intervention over another-as such barriers to implementation are generally unexpected and often not related to the actual type of intervention performed.
There are several limitations to consider when interpreting our study's findings. Firstly, cost and project activity data available (level of data) for each project were highly heterogenous in that systematic assessment of activity-based costs and declassification cost data (e.g. capital assets, fixed and variable costs) across the projects were not possible. As such, we were only able to use simplified apportionment criteria to allocate total costs for major programmatic activities only: case finding, treatment initiation (linkage to care), and treatment management. This may have resulted in over-estimation of costs as certain capital assets and fixed cost items may have value beyond project activity (e.g. laboratory equipment). Additionally, several Wave 5 projects reported that provision of (e.g., to contacts of TB patients identified during ACF campaign) TB preventive therapy (TPT). However, we were unable to categorically assess costs nor performance outcomes explicitly for activities relating to TPT from data sources available to our team, including project's financial statements. Therefore, misclassification of TPT related costs, while it may be small, may have resulted in over-estimation of CERs for these projects. We also were not able to extract quantifiable data on human resource involvement that are were not compensated through the project budget (e.g. routine healthcare worker tasked to screen TB symptoms at antenatal clinic for ICF). In this regard, our results may be an under-estimate of real-world human resource costs of ACF interventions. These data limitations in our study shed light on the need to improve and standardize the types and depth of the data necessary to perform comparative in-depth empiric cost and cost-effectiveness assessment of ACF interventions [9,14,15]. In particular, the cost per person screened (or initiating treatment) is also useful to programs for budgeting purposes and should be a priority for future research.
Secondly, we did not use estimates of health utility such as QALYs and DALYs, as conversion from cases detected to these measures is inherently context-specific. Therefore, our study findings may not be comparable to projects with other health outcomes. However, the cost per TB case detected is arguably the most direct and readily calculated measure of ACF performance without the need to make additional strong assumptions (e.g., future trajectory of people with TB who are not detected by ACF, impact on transmission of delayed diagnosis). Earlier analyses that do make these assumptions suggest that, in high-burden countries where missed TB diagnosis is common, the cost per case detected is about 25% higher than the cost per DALY averted (when considered over a 10-year time horizon) [6]. Thus, our cost-effectiveness ratios in terms of cost per case detected are likely to be modestly higher than the corresponding cost per DALY averted-but still well within most country-specific cost-effectiveness thresholds [12].
Thirdly, a simplified, standardized analytic approach taken in this study can be helpful for high-level comparisons on factors influencing cost-effectiveness (or costs associated programmatic performance) of interventions with similar/same objective. However, as described in our study, ACF projects are becoming more diversified to address gaps in the complex TB care cascade that are specific to the needs in the settings where these projects are implemented. Therefore, more detailed evaluation that includes projection of future benefits of key programmatic components (e.g., TPT provision to contacts of TB patients identified through ACF interventions or treatment linkages and adherence programs) is needed for each individual projects to precisely estimate the overall value and incremental cost-effectiveness of ACF interventions in each context [6,7,[15][16][17][18].
In conclusion, this systematic comparative analysis demonstrates that the costs and costeffectiveness of projects designed to improve the cascade of TB care are heterogeneous and context specific. This heterogeneity reflects the diversity of programmatic approaches and designs, target populations, and local settings in which these interventions are implemented. As future interventions introduce novel technology and processes, efforts to collect more detailed data that can provide insights on mechanistic, epidemiological, and operational factors influencing costs and impact of ACF interventions on the TB care cascade should be prioritized. Such data will be useful to improve our understanding of the costs and costeffectiveness of interventions-including those interventions that fail to achieve targets for cost-effectiveness. In the meantime, projects to strengthen the TB care cascade-if implemented in locally relevant fashion-appear to offer reasonable value for money and should continue to be prioritized as part of a comprehensive approach to ending TB in high-burden settings.